Overview

Dataset statistics

Number of variables36
Number of observations7024
Missing cells148130
Missing cells (%)58.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory333.6 B

Variable types

Numeric26
Categorical9
Unsupported1

Alerts

sofa_coagulation has constant value "0.0"Constant
sofa_liver has constant value "0.0"Constant
charttime has a high cardinality: 6988 distinct valuesHigh cardinality
icu_intime has a high cardinality: 1923 distinct valuesHigh cardinality
vent_start has a high cardinality: 1922 distinct valuesHigh cardinality
vent_end has a high cardinality: 1918 distinct valuesHigh cardinality
total_protein has 6930 (98.7%) missing valuesMissing
calcium has 933 (13.3%) missing valuesMissing
creatinine has 261 (3.7%) missing valuesMissing
glucose has 444 (6.3%) missing valuesMissing
sodium has 214 (3.0%) missing valuesMissing
chloride has 241 (3.4%) missing valuesMissing
heart_rate has 6833 (97.3%) missing valuesMissing
sbp has 6895 (98.2%) missing valuesMissing
dbp has 6895 (98.2%) missing valuesMissing
mbp has 6887 (98.0%) missing valuesMissing
resp_rate has 6832 (97.3%) missing valuesMissing
temperature has 6974 (99.3%) missing valuesMissing
hemoglobin has 1179 (16.8%) missing valuesMissing
wbc has 1207 (17.2%) missing valuesMissing
alt has 3964 (56.4%) missing valuesMissing
ast has 3936 (56.0%) missing valuesMissing
alp has 3976 (56.6%) missing valuesMissing
bilirubin_total has 3957 (56.3%) missing valuesMissing
bilirubin_direct has 6808 (96.9%) missing valuesMissing
bilirubin_indirect has 6812 (97.0%) missing valuesMissing
ph has 7004 (99.7%) missing valuesMissing
lactate has 7012 (99.8%) missing valuesMissing
pt has 3068 (43.7%) missing valuesMissing
urineoutput has 6942 (98.8%) missing valuesMissing
sofa_respiration has 7005 (99.7%) missing valuesMissing
sofa_coagulation has 7023 (> 99.9%) missing valuesMissing
sofa_liver has 7023 (> 99.9%) missing valuesMissing
sofa_cardiovascular has 6872 (97.8%) missing valuesMissing
sofa_cns has 6979 (99.4%) missing valuesMissing
sofa_renal has 7024 (100.0%) missing valuesMissing
charttime is uniformly distributedUniform
sofa_renal is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-03-22 07:37:25.294624
Analysis finished2023-03-22 07:38:05.541861
Duration40.25 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

stay_id
Real number (ℝ)

Distinct1923
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34978923
Minimum30004144
Maximum39992167
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:05.598323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum30004144
5-th percentile30515259
Q132550702
median34969901
Q337460808
95-th percentile39559375
Maximum39992167
Range9988023
Interquartile range (IQR)4910106

Descriptive statistics

Standard deviation2832324.6
Coefficient of variation (CV)0.080972323
Kurtosis-1.1604944
Mean34978923
Median Absolute Deviation (MAD)2466923
Skewness3.0447661 × 10-5
Sum2.4569195 × 1011
Variance8.0220628 × 1012
MonotonicityNot monotonic
2023-03-22T15:38:05.673811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37607624 196
 
2.8%
30515259 133
 
1.9%
33572547 93
 
1.3%
34776632 82
 
1.2%
36606626 62
 
0.9%
39598576 61
 
0.9%
34231127 49
 
0.7%
31986541 46
 
0.7%
36981368 44
 
0.6%
31526530 41
 
0.6%
Other values (1913) 6217
88.5%
ValueCountFrequency (%)
30004144 2
 
< 0.1%
30005366 1
 
< 0.1%
30006983 2
 
< 0.1%
30023204 3
 
< 0.1%
30031418 1
 
< 0.1%
30033048 1
 
< 0.1%
30034749 8
0.1%
30037986 3
 
< 0.1%
30045078 19
0.3%
30045159 2
 
< 0.1%
ValueCountFrequency (%)
39992167 2
< 0.1%
39986206 2
< 0.1%
39985110 3
< 0.1%
39982332 1
 
< 0.1%
39977971 4
0.1%
39972274 2
< 0.1%
39969519 3
< 0.1%
39969229 1
 
< 0.1%
39964899 2
< 0.1%
39961682 3
< 0.1%

charttime
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct6988
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size109.8 KiB
2120-04-18 06:04:00.000
 
16
2171-03-31 00:56:00.000
 
8
2181-08-08 19:53:00.000
 
2
2149-01-07 05:40:00.000
 
2
2135-05-10 03:34:00.000
 
2
Other values (6983)
6994 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters161552
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6972 ?
Unique (%)99.3%

Sample

1st row2148-12-27 18:15:00.000
2nd row2148-12-24 16:25:00.000
3rd row2148-12-25 09:12:00.000
4th row2149-01-01 17:37:00.000
5th row2148-12-27 08:40:00.000

Common Values

ValueCountFrequency (%)
2120-04-18 06:04:00.000 16
 
0.2%
2171-03-31 00:56:00.000 8
 
0.1%
2181-08-08 19:53:00.000 2
 
< 0.1%
2149-01-07 05:40:00.000 2
 
< 0.1%
2135-05-10 03:34:00.000 2
 
< 0.1%
2147-12-30 03:57:00.000 2
 
< 0.1%
2144-04-26 01:49:00.000 2
 
< 0.1%
2166-07-25 02:08:00.000 2
 
< 0.1%
2192-10-17 04:53:00.000 2
 
< 0.1%
2169-12-02 08:30:00.000 2
 
< 0.1%
Other values (6978) 6984
99.4%

Length

2023-03-22T15:38:05.738696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00.000 280
 
2.0%
06:00:00.000 78
 
0.6%
05:30:00.000 57
 
0.4%
06:30:00.000 52
 
0.4%
05:00:00.000 50
 
0.4%
07:00:00.000 50
 
0.4%
06:15:00.000 49
 
0.3%
06:10:00.000 41
 
0.3%
06:05:00.000 40
 
0.3%
05:40:00.000 38
 
0.3%
Other values (6155) 13313
94.8%

Most occurring characters

ValueCountFrequency (%)
0 53614
33.2%
1 18544
 
11.5%
2 15670
 
9.7%
- 14048
 
8.7%
: 14048
 
8.7%
7024
 
4.3%
. 7024
 
4.3%
5 6017
 
3.7%
3 5419
 
3.4%
4 5184
 
3.2%
Other values (4) 14960
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119408
73.9%
Other Punctuation 21072
 
13.0%
Dash Punctuation 14048
 
8.7%
Space Separator 7024
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 53614
44.9%
1 18544
 
15.5%
2 15670
 
13.1%
5 6017
 
5.0%
3 5419
 
4.5%
4 5184
 
4.3%
8 3868
 
3.2%
6 3848
 
3.2%
7 3824
 
3.2%
9 3420
 
2.9%
Other Punctuation
ValueCountFrequency (%)
: 14048
66.7%
. 7024
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 14048
100.0%
Space Separator
ValueCountFrequency (%)
7024
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 161552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 53614
33.2%
1 18544
 
11.5%
2 15670
 
9.7%
- 14048
 
8.7%
: 14048
 
8.7%
7024
 
4.3%
. 7024
 
4.3%
5 6017
 
3.7%
3 5419
 
3.4%
4 5184
 
3.2%
Other values (4) 14960
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 161552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 53614
33.2%
1 18544
 
11.5%
2 15670
 
9.7%
- 14048
 
8.7%
: 14048
 
8.7%
7024
 
4.3%
. 7024
 
4.3%
5 6017
 
3.7%
3 5419
 
3.4%
4 5184
 
3.2%
Other values (4) 14960
 
9.3%

total_protein
Real number (ℝ)

Distinct37
Distinct (%)39.4%
Missing6930
Missing (%)98.7%
Infinite0
Infinite (%)0.0%
Mean6.0404255
Minimum3.2
Maximum10.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:05.797122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.2
5-th percentile4.43
Q15.4
median6
Q36.6
95-th percentile7.535
Maximum10.2
Range7
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.1043725
Coefficient of variation (CV)0.18283024
Kurtosis2.4174225
Mean6.0404255
Median Absolute Deviation (MAD)0.6
Skewness0.69189225
Sum567.8
Variance1.2196385
MonotonicityNot monotonic
2023-03-22T15:38:05.860289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5.9 8
 
0.1%
6.2 7
 
0.1%
5.6 6
 
0.1%
5.4 5
 
0.1%
6.4 4
 
0.1%
5.7 4
 
0.1%
6 4
 
0.1%
6.1 4
 
0.1%
6.7 3
 
< 0.1%
7.4 3
 
< 0.1%
Other values (27) 46
 
0.7%
(Missing) 6930
98.7%
ValueCountFrequency (%)
3.2 1
 
< 0.1%
3.9 2
< 0.1%
4.1 1
 
< 0.1%
4.3 1
 
< 0.1%
4.5 1
 
< 0.1%
4.6 3
< 0.1%
4.7 2
< 0.1%
4.8 1
 
< 0.1%
4.9 1
 
< 0.1%
5 3
< 0.1%
ValueCountFrequency (%)
10.2 1
 
< 0.1%
9.5 1
 
< 0.1%
8.9 1
 
< 0.1%
7.6 2
< 0.1%
7.5 1
 
< 0.1%
7.4 3
< 0.1%
7.3 2
< 0.1%
7.2 2
< 0.1%
7.1 1
 
< 0.1%
6.9 3
< 0.1%

calcium
Real number (ℝ)

Distinct72
Distinct (%)1.2%
Missing933
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean8.3896405
Minimum4.2
Maximum12.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:05.932483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4.2
5-th percentile7.1
Q17.9
median8.4
Q38.9
95-th percentile9.7
Maximum12.3
Range8.1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.82006611
Coefficient of variation (CV)0.097747468
Kurtosis1.4719158
Mean8.3896405
Median Absolute Deviation (MAD)0.5
Skewness-0.12274857
Sum51101.3
Variance0.67250843
MonotonicityNot monotonic
2023-03-22T15:38:06.005979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.6 356
 
5.1%
8.5 353
 
5.0%
8.4 333
 
4.7%
8.3 331
 
4.7%
8.2 330
 
4.7%
8.1 312
 
4.4%
8.7 311
 
4.4%
8.8 301
 
4.3%
8.9 243
 
3.5%
8 243
 
3.5%
Other values (62) 2978
42.4%
(Missing) 933
 
13.3%
ValueCountFrequency (%)
4.2 1
 
< 0.1%
4.3 1
 
< 0.1%
4.4 1
 
< 0.1%
5.1 1
 
< 0.1%
5.2 2
 
< 0.1%
5.3 2
 
< 0.1%
5.4 3
 
< 0.1%
5.5 3
 
< 0.1%
5.6 3
 
< 0.1%
5.7 10
0.1%
ValueCountFrequency (%)
12.3 2
 
< 0.1%
11.9 2
 
< 0.1%
11.8 2
 
< 0.1%
11.6 1
 
< 0.1%
11.5 1
 
< 0.1%
11.4 3
< 0.1%
11.3 3
< 0.1%
11.2 2
 
< 0.1%
11.1 2
 
< 0.1%
11 7
0.1%

creatinine
Real number (ℝ)

Distinct149
Distinct (%)2.2%
Missing261
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean1.6440189
Minimum0.1
Maximum19.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:06.085071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.8
median1.1
Q31.8
95-th percentile4.7
Maximum19.7
Range19.6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.839893
Coefficient of variation (CV)1.1191435
Kurtosis24.260356
Mean1.6440189
Median Absolute Deviation (MAD)0.4
Skewness4.2113923
Sum11118.5
Variance3.3852064
MonotonicityNot monotonic
2023-03-22T15:38:06.154948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 593
 
8.4%
0.7 562
 
8.0%
0.9 516
 
7.3%
1 473
 
6.7%
0.6 465
 
6.6%
1.1 375
 
5.3%
1.2 359
 
5.1%
0.5 311
 
4.4%
1.3 268
 
3.8%
1.4 260
 
3.7%
Other values (139) 2581
36.7%
(Missing) 261
 
3.7%
ValueCountFrequency (%)
0.1 5
 
0.1%
0.2 47
 
0.7%
0.3 113
 
1.6%
0.4 185
 
2.6%
0.5 311
4.4%
0.6 465
6.6%
0.7 562
8.0%
0.8 593
8.4%
0.9 516
7.3%
1 473
6.7%
ValueCountFrequency (%)
19.7 1
< 0.1%
19.5 1
< 0.1%
19.4 1
< 0.1%
18.8 1
< 0.1%
18.7 2
< 0.1%
18.2 1
< 0.1%
17.3 1
< 0.1%
17.2 1
< 0.1%
17.1 1
< 0.1%
16.8 1
< 0.1%

glucose
Real number (ℝ)

Distinct389
Distinct (%)5.9%
Missing444
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean142.28967
Minimum30
Maximum2970
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:06.229258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile77
Q1102
median125
Q3159
95-th percentile261
Maximum2970
Range2940
Interquartile range (IQR)57

Descriptive statistics

Standard deviation89.875986
Coefficient of variation (CV)0.631641
Kurtosis288.86717
Mean142.28967
Median Absolute Deviation (MAD)27
Skewness12.421852
Sum936266
Variance8077.6929
MonotonicityNot monotonic
2023-03-22T15:38:06.297392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 93
 
1.3%
103 90
 
1.3%
105 89
 
1.3%
119 82
 
1.2%
98 79
 
1.1%
102 78
 
1.1%
113 78
 
1.1%
101 77
 
1.1%
107 76
 
1.1%
100 75
 
1.1%
Other values (379) 5763
82.0%
(Missing) 444
 
6.3%
ValueCountFrequency (%)
30 1
 
< 0.1%
31 1
 
< 0.1%
34 1
 
< 0.1%
35 4
0.1%
36 1
 
< 0.1%
37 1
 
< 0.1%
38 1
 
< 0.1%
39 2
< 0.1%
40 1
 
< 0.1%
41 2
< 0.1%
ValueCountFrequency (%)
2970 1
< 0.1%
2230 1
< 0.1%
2172 1
< 0.1%
2059 1
< 0.1%
1648 1
< 0.1%
1430 1
< 0.1%
1277 1
< 0.1%
939 1
< 0.1%
805 1
< 0.1%
746 1
< 0.1%

sodium
Real number (ℝ)

Distinct61
Distinct (%)0.9%
Missing214
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean137.30954
Minimum83
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:06.369709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum83
5-th percentile128
Q1134
median138
Q3141
95-th percentile145
Maximum185
Range102
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.6383842
Coefficient of variation (CV)0.041063308
Kurtosis5.7502716
Mean137.30954
Median Absolute Deviation (MAD)3
Skewness-0.39586924
Sum935078
Variance31.791376
MonotonicityNot monotonic
2023-03-22T15:38:06.441718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
139 609
 
8.7%
138 580
 
8.3%
137 547
 
7.8%
140 535
 
7.6%
141 501
 
7.1%
136 488
 
6.9%
135 433
 
6.2%
142 397
 
5.7%
134 365
 
5.2%
143 312
 
4.4%
Other values (51) 2043
29.1%
ValueCountFrequency (%)
83 1
 
< 0.1%
88 1
 
< 0.1%
106 2
 
< 0.1%
108 2
 
< 0.1%
109 1
 
< 0.1%
111 1
 
< 0.1%
112 1
 
< 0.1%
115 2
 
< 0.1%
116 9
0.1%
117 6
0.1%
ValueCountFrequency (%)
185 1
 
< 0.1%
179 1
 
< 0.1%
177 1
 
< 0.1%
172 1
 
< 0.1%
170 1
 
< 0.1%
167 1
 
< 0.1%
165 1
 
< 0.1%
164 2
< 0.1%
162 1
 
< 0.1%
160 4
0.1%

chloride
Real number (ℝ)

Distinct63
Distinct (%)0.9%
Missing241
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean102.06708
Minimum62
Maximum153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:06.512482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile91
Q198
median102
Q3106
95-th percentile112
Maximum153
Range91
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.7310081
Coefficient of variation (CV)0.065946906
Kurtosis2.3296411
Mean102.06708
Median Absolute Deviation (MAD)4
Skewness0.015507099
Sum692321
Variance45.30647
MonotonicityNot monotonic
2023-03-22T15:38:06.580050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103 505
 
7.2%
104 472
 
6.7%
102 457
 
6.5%
101 451
 
6.4%
105 428
 
6.1%
106 395
 
5.6%
100 386
 
5.5%
99 353
 
5.0%
107 311
 
4.4%
98 297
 
4.2%
Other values (53) 2728
38.8%
ValueCountFrequency (%)
62 1
< 0.1%
69 1
< 0.1%
70 2
< 0.1%
72 1
< 0.1%
73 1
< 0.1%
74 1
< 0.1%
77 2
< 0.1%
78 1
< 0.1%
80 2
< 0.1%
81 2
< 0.1%
ValueCountFrequency (%)
153 1
 
< 0.1%
151 1
 
< 0.1%
148 1
 
< 0.1%
137 2
 
< 0.1%
132 2
 
< 0.1%
130 1
 
< 0.1%
129 1
 
< 0.1%
127 2
 
< 0.1%
126 1
 
< 0.1%
125 5
0.1%

heart_rate
Real number (ℝ)

Distinct71
Distinct (%)37.2%
Missing6833
Missing (%)97.3%
Infinite0
Infinite (%)0.0%
Mean91.565445
Minimum48
Maximum152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:06.652364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q176.5
median90
Q3106.5
95-th percentile126
Maximum152
Range104
Interquartile range (IQR)30

Descriptive statistics

Standard deviation20.303089
Coefficient of variation (CV)0.22173309
Kurtosis-0.24649329
Mean91.565445
Median Absolute Deviation (MAD)15
Skewness0.40452526
Sum17489
Variance412.21543
MonotonicityNot monotonic
2023-03-22T15:38:06.720880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 8
 
0.1%
110 8
 
0.1%
100 7
 
0.1%
97 6
 
0.1%
75 6
 
0.1%
83 6
 
0.1%
67 5
 
0.1%
78 5
 
0.1%
70 5
 
0.1%
82 4
 
0.1%
Other values (61) 131
 
1.9%
(Missing) 6833
97.3%
ValueCountFrequency (%)
48 1
 
< 0.1%
54 1
 
< 0.1%
58 1
 
< 0.1%
60 1
 
< 0.1%
61 4
0.1%
62 4
0.1%
63 1
 
< 0.1%
64 2
< 0.1%
65 4
0.1%
66 2
< 0.1%
ValueCountFrequency (%)
152 2
< 0.1%
141 1
 
< 0.1%
134 1
 
< 0.1%
133 2
< 0.1%
129 1
 
< 0.1%
128 1
 
< 0.1%
126 3
< 0.1%
124 1
 
< 0.1%
122 4
0.1%
120 1
 
< 0.1%

sbp
Real number (ℝ)

Distinct70
Distinct (%)54.3%
Missing6895
Missing (%)98.2%
Infinite0
Infinite (%)0.0%
Mean119.53101
Minimum45
Maximum167
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:06.789050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile86.4
Q1104
median119
Q3132
95-th percentile160.2
Maximum167
Range122
Interquartile range (IQR)28

Descriptive statistics

Standard deviation22.348251
Coefficient of variation (CV)0.18696614
Kurtosis0.16446184
Mean119.53101
Median Absolute Deviation (MAD)14
Skewness0.025750894
Sum15419.5
Variance499.44434
MonotonicityNot monotonic
2023-03-22T15:38:06.858753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130 4
 
0.1%
95 4
 
0.1%
120 4
 
0.1%
97 4
 
0.1%
132 3
 
< 0.1%
118 3
 
< 0.1%
128 3
 
< 0.1%
104 3
 
< 0.1%
144 3
 
< 0.1%
125 3
 
< 0.1%
Other values (60) 95
 
1.4%
(Missing) 6895
98.2%
ValueCountFrequency (%)
45 1
 
< 0.1%
75 1
 
< 0.1%
76 1
 
< 0.1%
79 1
 
< 0.1%
82 1
 
< 0.1%
85 1
 
< 0.1%
86 1
 
< 0.1%
87 1
 
< 0.1%
89 1
 
< 0.1%
93 3
< 0.1%
ValueCountFrequency (%)
167 1
 
< 0.1%
164 1
 
< 0.1%
163 2
< 0.1%
162 2
< 0.1%
161 1
 
< 0.1%
159 3
< 0.1%
158 1
 
< 0.1%
157 1
 
< 0.1%
155 1
 
< 0.1%
152 1
 
< 0.1%

dbp
Real number (ℝ)

Distinct56
Distinct (%)43.4%
Missing6895
Missing (%)98.2%
Infinite0
Infinite (%)0.0%
Mean63.341085
Minimum22
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:06.930330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile42
Q154
median62
Q372
95-th percentile90.2
Maximum106
Range84
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.976412
Coefficient of variation (CV)0.23644072
Kurtosis0.31249588
Mean63.341085
Median Absolute Deviation (MAD)9
Skewness0.43979599
Sum8171
Variance224.29291
MonotonicityNot monotonic
2023-03-22T15:38:06.998550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 8
 
0.1%
63 6
 
0.1%
62 6
 
0.1%
49 5
 
0.1%
64 5
 
0.1%
60 5
 
0.1%
57 5
 
0.1%
72 4
 
0.1%
69 4
 
0.1%
80 4
 
0.1%
Other values (46) 77
 
1.1%
(Missing) 6895
98.2%
ValueCountFrequency (%)
22 1
 
< 0.1%
37 1
 
< 0.1%
38 1
 
< 0.1%
40 2
< 0.1%
41 1
 
< 0.1%
42 2
< 0.1%
43 1
 
< 0.1%
44 1
 
< 0.1%
45 2
< 0.1%
46 3
< 0.1%
ValueCountFrequency (%)
106 1
< 0.1%
103 1
< 0.1%
100 1
< 0.1%
99 1
< 0.1%
92 2
< 0.1%
91 1
< 0.1%
89 2
< 0.1%
88 1
< 0.1%
87 1
< 0.1%
85 1
< 0.1%

mbp
Real number (ℝ)

Distinct54
Distinct (%)39.4%
Missing6887
Missing (%)98.0%
Infinite0
Infinite (%)0.0%
Mean78.510949
Minimum28
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:07.069833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile56
Q169
median78
Q389
95-th percentile106
Maximum120
Range92
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.968284
Coefficient of variation (CV)0.19065218
Kurtosis0.6690595
Mean78.510949
Median Absolute Deviation (MAD)10
Skewness0.1657727
Sum10756
Variance224.04951
MonotonicityNot monotonic
2023-03-22T15:38:07.137966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69 7
 
0.1%
91 7
 
0.1%
73 6
 
0.1%
78 5
 
0.1%
71 5
 
0.1%
82 5
 
0.1%
94 5
 
0.1%
80 5
 
0.1%
89 5
 
0.1%
76 5
 
0.1%
Other values (44) 82
 
1.2%
(Missing) 6887
98.0%
ValueCountFrequency (%)
28 1
 
< 0.1%
49 1
 
< 0.1%
50 1
 
< 0.1%
55 2
< 0.1%
56 4
0.1%
57 1
 
< 0.1%
58 2
< 0.1%
60 2
< 0.1%
61 2
< 0.1%
62 3
< 0.1%
ValueCountFrequency (%)
120 1
 
< 0.1%
118 1
 
< 0.1%
115 1
 
< 0.1%
114 1
 
< 0.1%
108 1
 
< 0.1%
106 3
< 0.1%
104 1
 
< 0.1%
101 2
< 0.1%
97 1
 
< 0.1%
96 1
 
< 0.1%

resp_rate
Real number (ℝ)

Distinct32
Distinct (%)16.7%
Missing6832
Missing (%)97.3%
Infinite0
Infinite (%)0.0%
Mean21.122396
Minimum6
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:07.200715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile13
Q117
median20
Q326
95-th percentile31.45
Maximum41
Range35
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.2193221
Coefficient of variation (CV)0.29444208
Kurtosis0.30145378
Mean21.122396
Median Absolute Deviation (MAD)4
Skewness0.61437071
Sum4055.5
Variance38.679967
MonotonicityNot monotonic
2023-03-22T15:38:07.259600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
20 18
 
0.3%
17 15
 
0.2%
19 13
 
0.2%
27 12
 
0.2%
21 12
 
0.2%
13 11
 
0.2%
18 11
 
0.2%
26 11
 
0.2%
15 10
 
0.1%
16 10
 
0.1%
Other values (22) 69
 
1.0%
(Missing) 6832
97.3%
ValueCountFrequency (%)
6 1
 
< 0.1%
10 1
 
< 0.1%
12 7
0.1%
13 11
0.2%
14 6
 
0.1%
15 10
0.1%
16 10
0.1%
17 15
0.2%
18 11
0.2%
19 13
0.2%
ValueCountFrequency (%)
41 1
 
< 0.1%
39 1
 
< 0.1%
38 1
 
< 0.1%
37 2
< 0.1%
36 1
 
< 0.1%
35 2
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
31 2
< 0.1%
30 4
0.1%

temperature
Real number (ℝ)

Distinct23
Distinct (%)46.0%
Missing6974
Missing (%)99.3%
Infinite0
Infinite (%)0.0%
Mean36.8856
Minimum35.83
Maximum37.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:07.320349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum35.83
5-th percentile36.3025
Q136.67
median36.83
Q337.11
95-th percentile37.6205
Maximum37.94
Range2.11
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation0.41958755
Coefficient of variation (CV)0.011375376
Kurtosis0.23801665
Mean36.8856
Median Absolute Deviation (MAD)0.255
Skewness0.2571201
Sum1844.28
Variance0.17605371
MonotonicityNot monotonic
2023-03-22T15:38:07.373013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
36.72 5
 
0.1%
36.83 4
 
0.1%
37.11 4
 
0.1%
36.67 4
 
0.1%
36.89 3
 
< 0.1%
36.61 3
 
< 0.1%
36.44 3
 
< 0.1%
37.67 2
 
< 0.1%
37.56 2
 
< 0.1%
37.28 2
 
< 0.1%
Other values (13) 18
 
0.3%
(Missing) 6974
99.3%
ValueCountFrequency (%)
35.83 1
 
< 0.1%
36.28 2
 
< 0.1%
36.33 2
 
< 0.1%
36.39 1
 
< 0.1%
36.44 3
< 0.1%
36.61 3
< 0.1%
36.67 4
0.1%
36.72 5
0.1%
36.78 2
 
< 0.1%
36.83 4
0.1%
ValueCountFrequency (%)
37.94 1
 
< 0.1%
37.67 2
< 0.1%
37.56 2
< 0.1%
37.5 1
 
< 0.1%
37.39 1
 
< 0.1%
37.28 2
< 0.1%
37.2 2
< 0.1%
37.17 1
 
< 0.1%
37.11 4
0.1%
37.06 1
 
< 0.1%

hemoglobin
Real number (ℝ)

Distinct133
Distinct (%)2.3%
Missing1179
Missing (%)16.8%
Infinite0
Infinite (%)0.0%
Mean10.043353
Minimum3.9
Maximum18.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:07.435278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.9
5-th percentile7.1
Q18.4
median9.8
Q311.4
95-th percentile14.08
Maximum18.4
Range14.5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1775728
Coefficient of variation (CV)0.2168173
Kurtosis0.111423
Mean10.043353
Median Absolute Deviation (MAD)1.5
Skewness0.61470247
Sum58703.4
Variance4.7418231
MonotonicityNot monotonic
2023-03-22T15:38:07.505940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 126
 
1.8%
8.3 122
 
1.7%
7.9 117
 
1.7%
9.1 112
 
1.6%
8.4 111
 
1.6%
7.8 109
 
1.6%
8.8 108
 
1.5%
9.6 108
 
1.5%
9.9 107
 
1.5%
8.7 105
 
1.5%
Other values (123) 4720
67.2%
(Missing) 1179
 
16.8%
ValueCountFrequency (%)
3.9 1
 
< 0.1%
4.1 1
 
< 0.1%
4.2 3
< 0.1%
4.3 1
 
< 0.1%
5.2 3
< 0.1%
5.3 4
0.1%
5.4 1
 
< 0.1%
5.5 1
 
< 0.1%
5.6 3
< 0.1%
5.7 2
< 0.1%
ValueCountFrequency (%)
18.4 1
< 0.1%
18.3 1
< 0.1%
18.1 2
< 0.1%
18 1
< 0.1%
17.9 1
< 0.1%
17.7 2
< 0.1%
17.6 2
< 0.1%
17.5 2
< 0.1%
17.4 1
< 0.1%
17.3 1
< 0.1%

wbc
Real number (ℝ)

Distinct402
Distinct (%)6.9%
Missing1207
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean10.97313
Minimum0.1
Maximum125.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:07.579336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.9
Q15.9
median9.4
Q314.2
95-th percentile24.4
Maximum125.2
Range125.1
Interquartile range (IQR)8.3

Descriptive statistics

Standard deviation8.2288066
Coefficient of variation (CV)0.74990511
Kurtosis23.395371
Mean10.97313
Median Absolute Deviation (MAD)4
Skewness3.2001918
Sum63830.7
Variance67.713258
MonotonicityNot monotonic
2023-03-22T15:38:07.650904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 80
 
1.1%
7.8 66
 
0.9%
9.5 61
 
0.9%
8.5 57
 
0.8%
8.2 52
 
0.7%
7.9 52
 
0.7%
0.2 51
 
0.7%
9.3 51
 
0.7%
10.8 50
 
0.7%
9.9 50
 
0.7%
Other values (392) 5247
74.7%
(Missing) 1207
 
17.2%
ValueCountFrequency (%)
0.1 80
1.1%
0.2 51
0.7%
0.3 25
 
0.4%
0.4 22
 
0.3%
0.5 13
 
0.2%
0.6 9
 
0.1%
0.7 5
 
0.1%
0.8 4
 
0.1%
0.9 8
 
0.1%
1 4
 
0.1%
ValueCountFrequency (%)
125.2 1
< 0.1%
98.3 1
< 0.1%
96.3 1
< 0.1%
95.6 1
< 0.1%
95.1 1
< 0.1%
92.3 1
< 0.1%
83.9 1
< 0.1%
81.7 1
< 0.1%
76.8 1
< 0.1%
71.5 1
< 0.1%

alt
Real number (ℝ)

Distinct446
Distinct (%)14.6%
Missing3964
Missing (%)56.4%
Infinite0
Infinite (%)0.0%
Mean161.85915
Minimum1
Maximum15018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:07.721610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q117
median30
Q362
95-th percentile485.1
Maximum15018
Range15017
Interquartile range (IQR)45

Descriptive statistics

Standard deviation752.89883
Coefficient of variation (CV)4.6515679
Kurtosis153.27839
Mean161.85915
Median Absolute Deviation (MAD)16
Skewness10.896225
Sum495289
Variance566856.65
MonotonicityNot monotonic
2023-03-22T15:38:07.789822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 91
 
1.3%
16 88
 
1.3%
13 81
 
1.2%
18 80
 
1.1%
15 75
 
1.1%
10 72
 
1.0%
12 71
 
1.0%
25 71
 
1.0%
20 67
 
1.0%
22 67
 
1.0%
Other values (436) 2297
32.7%
(Missing) 3964
56.4%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 4
 
0.1%
3 3
 
< 0.1%
4 4
 
0.1%
5 13
 
0.2%
6 25
 
0.4%
7 38
0.5%
8 35
0.5%
9 50
0.7%
10 72
1.0%
ValueCountFrequency (%)
15018 1
< 0.1%
14795 1
< 0.1%
12576 1
< 0.1%
7767 1
< 0.1%
7695 1
< 0.1%
7677 1
< 0.1%
6815 1
< 0.1%
6799 1
< 0.1%
6780 1
< 0.1%
6701 1
< 0.1%

ast
Real number (ℝ)

Distinct508
Distinct (%)16.5%
Missing3936
Missing (%)56.0%
Infinite0
Infinite (%)0.0%
Mean243.88277
Minimum5
Maximum28275
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:07.862032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile12
Q122
median42
Q395
95-th percentile727.15
Maximum28275
Range28270
Interquartile range (IQR)73

Descriptive statistics

Standard deviation1216.5274
Coefficient of variation (CV)4.9881647
Kurtosis185.14566
Mean243.88277
Median Absolute Deviation (MAD)24
Skewness11.891535
Sum753110
Variance1479939
MonotonicityNot monotonic
2023-03-22T15:38:07.927621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 84
 
1.2%
16 74
 
1.1%
21 73
 
1.0%
17 67
 
1.0%
18 66
 
0.9%
23 64
 
0.9%
22 61
 
0.9%
14 60
 
0.9%
24 58
 
0.8%
13 55
 
0.8%
Other values (498) 2426
34.5%
(Missing) 3936
56.0%
ValueCountFrequency (%)
5 1
 
< 0.1%
6 5
 
0.1%
7 7
 
0.1%
8 18
 
0.3%
9 20
 
0.3%
10 21
 
0.3%
11 40
0.6%
12 46
0.7%
13 55
0.8%
14 60
0.9%
ValueCountFrequency (%)
28275 1
< 0.1%
22140 1
< 0.1%
18970 1
< 0.1%
15108 1
< 0.1%
14725 1
< 0.1%
14370 1
< 0.1%
14082 1
< 0.1%
12350 1
< 0.1%
11955 1
< 0.1%
11016 1
< 0.1%

alp
Real number (ℝ)

Distinct434
Distinct (%)14.2%
Missing3976
Missing (%)56.6%
Infinite0
Infinite (%)0.0%
Mean131.93373
Minimum7
Maximum1185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:07.997030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile42
Q165
median92
Q3149
95-th percentile368
Maximum1185
Range1178
Interquartile range (IQR)84

Descriptive statistics

Standard deviation123.0886
Coefficient of variation (CV)0.93295779
Kurtosis16.824376
Mean131.93373
Median Absolute Deviation (MAD)34
Skewness3.4832105
Sum402134
Variance15150.803
MonotonicityNot monotonic
2023-03-22T15:38:08.076226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 45
 
0.6%
68 38
 
0.5%
55 37
 
0.5%
83 36
 
0.5%
64 36
 
0.5%
63 36
 
0.5%
67 36
 
0.5%
84 34
 
0.5%
59 34
 
0.5%
90 34
 
0.5%
Other values (424) 2682
38.2%
(Missing) 3976
56.6%
ValueCountFrequency (%)
7 1
< 0.1%
8 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
20 2
< 0.1%
21 1
< 0.1%
23 1
< 0.1%
24 1
< 0.1%
25 1
< 0.1%
ValueCountFrequency (%)
1185 1
< 0.1%
1183 1
< 0.1%
1115 1
< 0.1%
1093 1
< 0.1%
1064 1
< 0.1%
1046 1
< 0.1%
951 1
< 0.1%
922 1
< 0.1%
919 1
< 0.1%
908 1
< 0.1%

bilirubin_total
Real number (ℝ)

Distinct309
Distinct (%)10.1%
Missing3957
Missing (%)56.3%
Infinite0
Infinite (%)0.0%
Mean4.2487447
Minimum0.1
Maximum52.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:08.149731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.5
median1
Q33.6
95-th percentile23.27
Maximum52.6
Range52.5
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation7.8080562
Coefficient of variation (CV)1.8377325
Kurtosis9.3783226
Mean4.2487447
Median Absolute Deviation (MAD)0.7
Skewness2.9810442
Sum13030.9
Variance60.965741
MonotonicityNot monotonic
2023-03-22T15:38:08.222523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 267
 
3.8%
0.5 250
 
3.6%
0.3 240
 
3.4%
0.7 168
 
2.4%
0.6 167
 
2.4%
0.2 149
 
2.1%
0.8 112
 
1.6%
0.9 110
 
1.6%
1 93
 
1.3%
1.1 81
 
1.2%
Other values (299) 1430
 
20.4%
(Missing) 3957
56.3%
ValueCountFrequency (%)
0.1 20
 
0.3%
0.2 149
2.1%
0.3 240
3.4%
0.4 267
3.8%
0.5 250
3.6%
0.6 167
2.4%
0.7 168
2.4%
0.8 112
1.6%
0.9 110
1.6%
1 93
 
1.3%
ValueCountFrequency (%)
52.6 1
< 0.1%
52.5 1
< 0.1%
51.4 1
< 0.1%
48.8 1
< 0.1%
48.3 1
< 0.1%
48.1 1
< 0.1%
47 1
< 0.1%
46.5 1
< 0.1%
46.4 2
< 0.1%
45.5 1
< 0.1%

bilirubin_direct
Real number (ℝ)

Distinct90
Distinct (%)41.7%
Missing6808
Missing (%)96.9%
Infinite0
Infinite (%)0.0%
Mean4.1453704
Minimum0.1
Maximum24.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:08.293559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q11.075
median2.65
Q35.925
95-th percentile14.5
Maximum24.9
Range24.8
Interquartile range (IQR)4.85

Descriptive statistics

Standard deviation4.5372235
Coefficient of variation (CV)1.0945279
Kurtosis3.9372989
Mean4.1453704
Median Absolute Deviation (MAD)1.95
Skewness1.9100379
Sum895.4
Variance20.586397
MonotonicityNot monotonic
2023-03-22T15:38:08.366064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 10
 
0.1%
1.2 8
 
0.1%
0.3 8
 
0.1%
0.6 7
 
0.1%
0.4 6
 
0.1%
1.6 6
 
0.1%
1.3 6
 
0.1%
2.7 6
 
0.1%
0.5 5
 
0.1%
0.9 5
 
0.1%
Other values (80) 149
 
2.1%
(Missing) 6808
96.9%
ValueCountFrequency (%)
0.1 4
 
0.1%
0.2 10
0.1%
0.3 8
0.1%
0.4 6
0.1%
0.5 5
0.1%
0.6 7
0.1%
0.7 4
 
0.1%
0.8 3
 
< 0.1%
0.9 5
0.1%
1 2
 
< 0.1%
ValueCountFrequency (%)
24.9 1
 
< 0.1%
21.9 1
 
< 0.1%
20.1 1
 
< 0.1%
20 1
 
< 0.1%
17.8 1
 
< 0.1%
16 1
 
< 0.1%
15.8 1
 
< 0.1%
15 3
< 0.1%
14.8 1
 
< 0.1%
14.4 1
 
< 0.1%

bilirubin_indirect
Real number (ℝ)

Distinct66
Distinct (%)31.1%
Missing6812
Missing (%)97.0%
Infinite0
Infinite (%)0.0%
Mean2.3490566
Minimum0.1
Maximum17.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:08.437592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.7
median1.4
Q33.1
95-th percentile7.635
Maximum17.1
Range17
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation2.5580556
Coefficient of variation (CV)1.0889715
Kurtosis6.2801194
Mean2.3490566
Median Absolute Deviation (MAD)0.9
Skewness2.1606367
Sum498
Variance6.5436484
MonotonicityNot monotonic
2023-03-22T15:38:08.508007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7 15
 
0.2%
0.4 10
 
0.1%
0.6 10
 
0.1%
1 10
 
0.1%
0.8 9
 
0.1%
0.3 8
 
0.1%
0.9 8
 
0.1%
0.2 8
 
0.1%
1.5 8
 
0.1%
1.9 7
 
0.1%
Other values (56) 119
 
1.7%
(Missing) 6812
97.0%
ValueCountFrequency (%)
0.1 7
0.1%
0.2 8
0.1%
0.3 8
0.1%
0.4 10
0.1%
0.5 3
 
< 0.1%
0.6 10
0.1%
0.7 15
0.2%
0.8 9
0.1%
0.9 8
0.1%
1 10
0.1%
ValueCountFrequency (%)
17.1 1
< 0.1%
12.8 1
< 0.1%
9.9 1
< 0.1%
9.6 1
< 0.1%
9.4 1
< 0.1%
8.8 1
< 0.1%
8.7 2
< 0.1%
8 1
< 0.1%
7.8 2
< 0.1%
7.5 1
< 0.1%

ph
Real number (ℝ)

Distinct14
Distinct (%)70.0%
Missing7004
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean7.358
Minimum7.09
Maximum7.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:08.570923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7.09
5-th percentile7.2325
Q17.3175
median7.355
Q37.4
95-th percentile7.48
Maximum7.48
Range0.39
Interquartile range (IQR)0.0825

Descriptive statistics

Standard deviation0.091110113
Coefficient of variation (CV)0.012382456
Kurtosis2.8457863
Mean7.358
Median Absolute Deviation (MAD)0.04
Skewness-1.1746416
Sum147.16
Variance0.0083010526
MonotonicityNot monotonic
2023-03-22T15:38:08.620987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
7.39 4
 
0.1%
7.35 2
 
< 0.1%
7.31 2
 
< 0.1%
7.48 2
 
< 0.1%
7.36 1
 
< 0.1%
7.29 1
 
< 0.1%
7.34 1
 
< 0.1%
7.32 1
 
< 0.1%
7.45 1
 
< 0.1%
7.43 1
 
< 0.1%
Other values (4) 4
 
0.1%
(Missing) 7004
99.7%
ValueCountFrequency (%)
7.09 1
 
< 0.1%
7.24 1
 
< 0.1%
7.29 1
 
< 0.1%
7.31 2
< 0.1%
7.32 1
 
< 0.1%
7.33 1
 
< 0.1%
7.34 1
 
< 0.1%
7.35 2
< 0.1%
7.36 1
 
< 0.1%
7.39 4
0.1%
ValueCountFrequency (%)
7.48 2
< 0.1%
7.47 1
 
< 0.1%
7.45 1
 
< 0.1%
7.43 1
 
< 0.1%
7.39 4
0.1%
7.36 1
 
< 0.1%
7.35 2
< 0.1%
7.34 1
 
< 0.1%
7.33 1
 
< 0.1%
7.32 1
 
< 0.1%

lactate
Real number (ℝ)

Distinct12
Distinct (%)100.0%
Missing7012
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean2.8416667
Minimum1
Maximum12.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:08.675597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.055
Q11.275
median1.85
Q33
95-th percentile7.645
Maximum12.1
Range11.1
Interquartile range (IQR)1.725

Descriptive statistics

Standard deviation3.0654848
Coefficient of variation (CV)1.078763
Kurtosis9.1523405
Mean2.8416667
Median Absolute Deviation (MAD)0.7
Skewness2.9169347
Sum34.1
Variance9.397197
MonotonicityNot monotonic
2023-03-22T15:38:08.725101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2.1 1
 
< 0.1%
1.9 1
 
< 0.1%
1.4 1
 
< 0.1%
4 1
 
< 0.1%
3.3 1
 
< 0.1%
1.1 1
 
< 0.1%
1.3 1
 
< 0.1%
1.8 1
 
< 0.1%
2.9 1
 
< 0.1%
1 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 7012
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
1.1 1
< 0.1%
1.2 1
< 0.1%
1.3 1
< 0.1%
1.4 1
< 0.1%
1.8 1
< 0.1%
1.9 1
< 0.1%
2.1 1
< 0.1%
2.9 1
< 0.1%
3.3 1
< 0.1%
ValueCountFrequency (%)
12.1 1
< 0.1%
4 1
< 0.1%
3.3 1
< 0.1%
2.9 1
< 0.1%
2.1 1
< 0.1%
1.9 1
< 0.1%
1.8 1
< 0.1%
1.4 1
< 0.1%
1.3 1
< 0.1%
1.2 1
< 0.1%

pt
Real number (ℝ)

Distinct420
Distinct (%)10.6%
Missing3068
Missing (%)43.7%
Infinite0
Infinite (%)0.0%
Mean18.826567
Minimum9.2
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:08.787566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum9.2
5-th percentile11.1
Q112.8
median14.9
Q320.4
95-th percentile38.3
Maximum150
Range140.8
Interquartile range (IQR)7.6

Descriptive statistics

Standard deviation11.588015
Coefficient of variation (CV)0.61551397
Kurtosis32.531609
Mean18.826567
Median Absolute Deviation (MAD)2.8
Skewness4.5166391
Sum74477.9
Variance134.28209
MonotonicityNot monotonic
2023-03-22T15:38:08.856333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.7 72
 
1.0%
12.8 61
 
0.9%
12.3 61
 
0.9%
12.9 60
 
0.9%
13.2 58
 
0.8%
12.5 58
 
0.8%
12.7 56
 
0.8%
13.4 56
 
0.8%
14.2 55
 
0.8%
13.1 54
 
0.8%
Other values (410) 3365
47.9%
(Missing) 3068
43.7%
ValueCountFrequency (%)
9.2 1
 
< 0.1%
9.3 3
 
< 0.1%
9.4 2
 
< 0.1%
9.5 4
0.1%
9.6 1
 
< 0.1%
9.7 3
 
< 0.1%
9.8 7
0.1%
9.9 3
 
< 0.1%
10 8
0.1%
10.1 6
0.1%
ValueCountFrequency (%)
150 2
 
< 0.1%
130.9 8
0.1%
118 1
 
< 0.1%
110.8 1
 
< 0.1%
101.1 1
 
< 0.1%
96.4 1
 
< 0.1%
94.6 1
 
< 0.1%
91.1 1
 
< 0.1%
85.5 1
 
< 0.1%
83.6 1
 
< 0.1%

urineoutput
Real number (ℝ)

Distinct35
Distinct (%)42.7%
Missing6942
Missing (%)98.8%
Infinite0
Infinite (%)0.0%
Mean86.573171
Minimum10
Maximum380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:08.925670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15.15
Q135
median60
Q3123.75
95-th percentile219
Maximum380
Range370
Interquartile range (IQR)88.75

Descriptive statistics

Standard deviation70.872173
Coefficient of variation (CV)0.81863899
Kurtosis3.2001711
Mean86.573171
Median Absolute Deviation (MAD)40
Skewness1.6012521
Sum7099
Variance5022.865
MonotonicityNot monotonic
2023-03-22T15:38:08.986698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
100 8
 
0.1%
60 8
 
0.1%
25 5
 
0.1%
40 5
 
0.1%
30 5
 
0.1%
50 4
 
0.1%
125 4
 
0.1%
20 3
 
< 0.1%
45 3
 
< 0.1%
150 3
 
< 0.1%
Other values (25) 34
 
0.5%
(Missing) 6942
98.8%
ValueCountFrequency (%)
10 1
 
< 0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
15 2
 
< 0.1%
18 2
 
< 0.1%
20 3
< 0.1%
25 5
0.1%
30 5
0.1%
35 2
 
< 0.1%
40 5
0.1%
ValueCountFrequency (%)
380 1
< 0.1%
275 2
< 0.1%
250 1
< 0.1%
220 1
< 0.1%
200 1
< 0.1%
185 1
< 0.1%
180 1
< 0.1%
175 1
< 0.1%
170 1
< 0.1%
160 2
< 0.1%

sofa_respiration
Categorical

Distinct3
Distinct (%)15.8%
Missing7005
Missing (%)99.7%
Memory size109.8 KiB
2.0
14 
1.0
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters57
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 14
 
0.2%
1.0 3
 
< 0.1%
0.0 2
 
< 0.1%
(Missing) 7005
99.7%

Length

2023-03-22T15:38:09.047937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:38:09.116920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 14
73.7%
1.0 3
 
15.8%
0.0 2
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 21
36.8%
. 19
33.3%
2 14
24.6%
1 3
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38
66.7%
Other Punctuation 19
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21
55.3%
2 14
36.8%
1 3
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21
36.8%
. 19
33.3%
2 14
24.6%
1 3
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21
36.8%
. 19
33.3%
2 14
24.6%
1 3
 
5.3%

sofa_coagulation
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing7023
Missing (%)> 99.9%
Memory size109.8 KiB
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.0

Common Values

ValueCountFrequency (%)
0.0 1
 
< 0.1%
(Missing) 7023
> 99.9%

Length

2023-03-22T15:38:09.167174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:38:09.222195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

sofa_liver
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing7023
Missing (%)> 99.9%
Memory size109.8 KiB
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row0.0

Common Values

ValueCountFrequency (%)
0.0 1
 
< 0.1%
(Missing) 7023
> 99.9%

Length

2023-03-22T15:38:09.263337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:38:09.311758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2
66.7%
. 1
33.3%
Distinct5
Distinct (%)3.3%
Missing6872
Missing (%)97.8%
Memory size109.8 KiB
0.0
105 
1.0
33 
4.0
 
7
3.0
 
6
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters456
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 105
 
1.5%
1.0 33
 
0.5%
4.0 7
 
0.1%
3.0 6
 
0.1%
2.0 1
 
< 0.1%
(Missing) 6872
97.8%

Length

2023-03-22T15:38:09.353759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:38:09.411542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 105
69.1%
1.0 33
 
21.7%
4.0 7
 
4.6%
3.0 6
 
3.9%
2.0 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 257
56.4%
. 152
33.3%
1 33
 
7.2%
4 7
 
1.5%
3 6
 
1.3%
2 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 304
66.7%
Other Punctuation 152
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 257
84.5%
1 33
 
10.9%
4 7
 
2.3%
3 6
 
2.0%
2 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 152
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 456
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 257
56.4%
. 152
33.3%
1 33
 
7.2%
4 7
 
1.5%
3 6
 
1.3%
2 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 257
56.4%
. 152
33.3%
1 33
 
7.2%
4 7
 
1.5%
3 6
 
1.3%
2 1
 
0.2%

sofa_cns
Categorical

Distinct5
Distinct (%)11.1%
Missing6979
Missing (%)99.4%
Memory size109.8 KiB
0.0
24 
1.0
16 
2.0
 
2
3.0
 
2
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters135
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 24
 
0.3%
1.0 16
 
0.2%
2.0 2
 
< 0.1%
3.0 2
 
< 0.1%
4.0 1
 
< 0.1%
(Missing) 6979
99.4%

Length

2023-03-22T15:38:09.462299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:38:09.520327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 24
53.3%
1.0 16
35.6%
2.0 2
 
4.4%
3.0 2
 
4.4%
4.0 1
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 69
51.1%
. 45
33.3%
1 16
 
11.9%
2 2
 
1.5%
3 2
 
1.5%
4 1
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 90
66.7%
Other Punctuation 45
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69
76.7%
1 16
 
17.8%
2 2
 
2.2%
3 2
 
2.2%
4 1
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 135
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 69
51.1%
. 45
33.3%
1 16
 
11.9%
2 2
 
1.5%
3 2
 
1.5%
4 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69
51.1%
. 45
33.3%
1 16
 
11.9%
2 2
 
1.5%
3 2
 
1.5%
4 1
 
0.7%

sofa_renal
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing7024
Missing (%)100.0%
Memory size109.8 KiB

icu_intime
Categorical

Distinct1923
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Memory size109.8 KiB
2196-01-29 21:14:38.000
 
196
2193-01-25 13:54:41.000
 
133
2134-08-16 11:17:21.000
 
93
2137-04-27 15:53:55.000
 
82
2168-12-21 14:04:01.000
 
62
Other values (1918)
6458 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters161552
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique619 ?
Unique (%)8.8%

Sample

1st row2149-01-01 16:50:07.000
2nd row2149-01-01 16:50:07.000
3rd row2149-01-01 16:50:07.000
4th row2149-01-01 16:50:07.000
5th row2149-01-01 16:50:07.000

Common Values

ValueCountFrequency (%)
2196-01-29 21:14:38.000 196
 
2.8%
2193-01-25 13:54:41.000 133
 
1.9%
2134-08-16 11:17:21.000 93
 
1.3%
2137-04-27 15:53:55.000 82
 
1.2%
2168-12-21 14:04:01.000 62
 
0.9%
2178-10-18 10:30:37.000 61
 
0.9%
2124-02-03 06:00:00.000 49
 
0.7%
2149-02-01 16:24:38.000 46
 
0.7%
2188-02-21 15:01:19.000 44
 
0.6%
2168-04-27 16:32:20.000 41
 
0.6%
Other values (1913) 6217
88.5%

Length

2023-03-22T15:38:09.574979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2196-01-29 196
 
1.4%
21:14:38.000 196
 
1.4%
2193-01-25 133
 
0.9%
13:54:41.000 133
 
0.9%
2134-08-16 93
 
0.7%
11:17:21.000 93
 
0.7%
2137-04-27 82
 
0.6%
15:53:55.000 82
 
0.6%
2168-12-21 62
 
0.4%
14:04:01.000 62
 
0.4%
Other values (3579) 12916
91.9%

Most occurring characters

ValueCountFrequency (%)
0 39211
24.3%
1 22726
14.1%
2 18324
11.3%
- 14048
 
8.7%
: 14048
 
8.7%
3 7275
 
4.5%
7024
 
4.3%
. 7024
 
4.3%
4 6971
 
4.3%
5 6493
 
4.0%
Other values (4) 18408
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119408
73.9%
Other Punctuation 21072
 
13.0%
Dash Punctuation 14048
 
8.7%
Space Separator 7024
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39211
32.8%
1 22726
19.0%
2 18324
15.3%
3 7275
 
6.1%
4 6971
 
5.8%
5 6493
 
5.4%
8 5242
 
4.4%
7 4515
 
3.8%
9 4401
 
3.7%
6 4250
 
3.6%
Other Punctuation
ValueCountFrequency (%)
: 14048
66.7%
. 7024
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 14048
100.0%
Space Separator
ValueCountFrequency (%)
7024
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 161552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39211
24.3%
1 22726
14.1%
2 18324
11.3%
- 14048
 
8.7%
: 14048
 
8.7%
3 7275
 
4.5%
7024
 
4.3%
. 7024
 
4.3%
4 6971
 
4.3%
5 6493
 
4.0%
Other values (4) 18408
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 161552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39211
24.3%
1 22726
14.1%
2 18324
11.3%
- 14048
 
8.7%
: 14048
 
8.7%
3 7275
 
4.5%
7024
 
4.3%
. 7024
 
4.3%
4 6971
 
4.3%
5 6493
 
4.0%
Other values (4) 18408
11.4%

vent_start
Categorical

Distinct1922
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Memory size109.8 KiB
1/30/96 11:00
 
196
1/26/93 12:00
 
133
8/18/34 20:00
 
93
4/29/37 13:00
 
82
12/22/68 12:52
 
62
Other values (1917)
6458 

Length

Max length14
Median length13
Mean length12.733628
Min length11

Characters and Unicode

Total characters89441
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique619 ?
Unique (%)8.8%

Sample

1st row1/3/49 18:00
2nd row1/3/49 18:00
3rd row1/3/49 18:00
4th row1/3/49 18:00
5th row1/3/49 18:00

Common Values

ValueCountFrequency (%)
1/30/96 11:00 196
 
2.8%
1/26/93 12:00 133
 
1.9%
8/18/34 20:00 93
 
1.3%
4/29/37 13:00 82
 
1.2%
12/22/68 12:52 62
 
0.9%
10/19/78 18:00 61
 
0.9%
2/4/24 4:00 49
 
0.7%
2/2/49 6:00 46
 
0.7%
2/23/88 20:00 44
 
0.6%
4/28/68 6:00 41
 
0.6%
Other values (1912) 6217
88.5%

Length

2023-03-22T15:38:09.630254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11:00 443
 
3.2%
12:00 394
 
2.8%
13:00 391
 
2.8%
20:00 383
 
2.7%
18:00 367
 
2.6%
15:00 312
 
2.2%
16:00 282
 
2.0%
17:00 272
 
1.9%
14:00 238
 
1.7%
4:00 230
 
1.6%
Other values (2286) 10736
76.4%

Most occurring characters

ValueCountFrequency (%)
0 14160
15.8%
/ 14048
15.7%
1 13152
14.7%
2 8342
9.3%
7024
7.9%
: 7024
7.9%
3 4617
 
5.2%
8 3978
 
4.4%
4 3836
 
4.3%
6 3514
 
3.9%
Other values (3) 9746
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61345
68.6%
Other Punctuation 21072
 
23.6%
Space Separator 7024
 
7.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14160
23.1%
1 13152
21.4%
2 8342
13.6%
3 4617
 
7.5%
8 3978
 
6.5%
4 3836
 
6.3%
6 3514
 
5.7%
5 3411
 
5.6%
9 3310
 
5.4%
7 3025
 
4.9%
Other Punctuation
ValueCountFrequency (%)
/ 14048
66.7%
: 7024
33.3%
Space Separator
ValueCountFrequency (%)
7024
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 89441
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14160
15.8%
/ 14048
15.7%
1 13152
14.7%
2 8342
9.3%
7024
7.9%
: 7024
7.9%
3 4617
 
5.2%
8 3978
 
4.4%
4 3836
 
4.3%
6 3514
 
3.9%
Other values (3) 9746
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14160
15.8%
/ 14048
15.7%
1 13152
14.7%
2 8342
9.3%
7024
7.9%
: 7024
7.9%
3 4617
 
5.2%
8 3978
 
4.4%
4 3836
 
4.3%
6 3514
 
3.9%
Other values (3) 9746
10.9%

vent_end
Categorical

Distinct1918
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Memory size109.8 KiB
1/30/96 13:00
 
196
1/26/93 16:00
 
133
8/19/34 12:00
 
93
4/30/37 4:00
 
82
12/26/68 21:00
 
62
Other values (1913)
6458 

Length

Max length14
Median length13
Mean length12.567483
Min length11

Characters and Unicode

Total characters88274
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique617 ?
Unique (%)8.8%

Sample

1st row1/3/49 20:00
2nd row1/3/49 20:00
3rd row1/3/49 20:00
4th row1/3/49 20:00
5th row1/3/49 20:00

Common Values

ValueCountFrequency (%)
1/30/96 13:00 196
 
2.8%
1/26/93 16:00 133
 
1.9%
8/19/34 12:00 93
 
1.3%
4/30/37 4:00 82
 
1.2%
12/26/68 21:00 62
 
0.9%
10/21/78 16:34 61
 
0.9%
2/8/24 15:00 49
 
0.7%
2/2/49 6:10 46
 
0.7%
2/24/88 7:00 44
 
0.6%
2/8/54 12:45 41
 
0.6%
Other values (1908) 6217
88.5%

Length

2023-03-22T15:38:09.693022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4:00 677
 
4.8%
16:00 513
 
3.7%
12:00 506
 
3.6%
8:00 485
 
3.5%
7:00 470
 
3.3%
11:00 430
 
3.1%
20:00 357
 
2.5%
15:00 322
 
2.3%
13:00 298
 
2.1%
0:00 256
 
1.8%
Other values (2168) 9734
69.3%

Most occurring characters

ValueCountFrequency (%)
0 15478
17.5%
/ 14048
15.9%
1 11737
13.3%
2 7220
8.2%
7024
8.0%
: 7024
8.0%
3 4555
 
5.2%
4 4006
 
4.5%
8 3948
 
4.5%
6 3761
 
4.3%
Other values (3) 9473
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60178
68.2%
Other Punctuation 21072
 
23.9%
Space Separator 7024
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15478
25.7%
1 11737
19.5%
2 7220
12.0%
3 4555
 
7.6%
4 4006
 
6.7%
8 3948
 
6.6%
6 3761
 
6.2%
5 3317
 
5.5%
7 3311
 
5.5%
9 2845
 
4.7%
Other Punctuation
ValueCountFrequency (%)
/ 14048
66.7%
: 7024
33.3%
Space Separator
ValueCountFrequency (%)
7024
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 88274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15478
17.5%
/ 14048
15.9%
1 11737
13.3%
2 7220
8.2%
7024
8.0%
: 7024
8.0%
3 4555
 
5.2%
4 4006
 
4.5%
8 3948
 
4.5%
6 3761
 
4.3%
Other values (3) 9473
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15478
17.5%
/ 14048
15.9%
1 11737
13.3%
2 7220
8.2%
7024
8.0%
: 7024
8.0%
3 4555
 
5.2%
4 4006
 
4.5%
8 3948
 
4.5%
6 3761
 
4.3%
Other values (3) 9473
10.7%

vent_duration
Real number (ℝ)

Distinct859
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.816797
Minimum0.016666667
Maximum604.16667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2023-03-22T15:38:09.756148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.016666667
5-th percentile2
Q18.6
median18.766667
Q347.816667
95-th percentile144.88333
Maximum604.16667
Range604.15
Interquartile range (IQR)39.216667

Descriptive statistics

Standard deviation56.264793
Coefficient of variation (CV)1.4130919
Kurtosis18.846432
Mean39.816797
Median Absolute Deviation (MAD)13.233333
Skewness3.5775406
Sum279673.18
Variance3165.7269
MonotonicityNot monotonic
2023-03-22T15:38:09.822466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 284
 
4.0%
4 215
 
3.1%
15 190
 
2.7%
16 169
 
2.4%
13 147
 
2.1%
18 139
 
2.0%
7 129
 
1.8%
10 105
 
1.5%
8 104
 
1.5%
6 98
 
1.4%
Other values (849) 5444
77.5%
ValueCountFrequency (%)
0.016666667 1
 
< 0.1%
0.033333333 3
 
< 0.1%
0.05 2
 
< 0.1%
0.166666667 52
0.7%
0.183333333 10
 
0.1%
0.2 3
 
< 0.1%
0.216666667 2
 
< 0.1%
0.266666667 1
 
< 0.1%
0.333333333 2
 
< 0.1%
0.4 2
 
< 0.1%
ValueCountFrequency (%)
604.1666667 2
 
< 0.1%
564 2
 
< 0.1%
527 2
 
< 0.1%
520 3
 
< 0.1%
493 2
 
< 0.1%
409 9
0.1%
405 10
0.1%
358.8666667 3
 
< 0.1%
354 2
 
< 0.1%
331.5 3
 
< 0.1%

Interactions

2023-03-22T15:38:03.114589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:26.475871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:28.064983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:29.467945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:30.937910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:32.521872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:33.955771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:35.508524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:36.971081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:38.399844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:39.706970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:41.049191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:42.531632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:43.890108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:45.219735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:46.739992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:48.422406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:49.914936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:51.351542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:52.786910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:54.507090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:55.824697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:57.251090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:58.542195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:59.790683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:38:01.248521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:38:03.173918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:26.560462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:28.116267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:29.528401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:30.999175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:32.581066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:34.016825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:35.565752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:37.021542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:38.453935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:39.755391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:41.104653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:42.584968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:43.938995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:45.280434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:46.798662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:48.482672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:49.972925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:51.410002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:52.845367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:54.561199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:55.879400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:57.302135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-03-22T15:38:01.127109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:38:02.990502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:38:04.523597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:28.005211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:29.416928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:30.871100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:32.458362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:33.895180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:35.449224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:36.911991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:38.351082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:39.657659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:40.998822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:42.484374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:43.832219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:45.168288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:46.677399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:48.362583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:49.853753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:51.292570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:52.725770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:54.448728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:55.773934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:57.197709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:58.490835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:37:59.742851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:38:01.180760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-22T15:38:03.052734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Missing values

2023-03-22T15:38:04.647018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-22T15:38:04.998441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-22T15:38:05.288798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

stay_idcharttimetotal_proteincalciumcreatinineglucosesodiumchlorideheart_ratesbpdbpmbpresp_ratetemperaturehemoglobinwbcaltastalpbilirubin_totalbilirubin_directbilirubin_indirectphlactatepturineoutputsofa_respirationsofa_coagulationsofa_liversofa_cardiovascularsofa_cnssofa_renalicu_intimevent_startvent_endvent_duration
0357155752148-12-27 18:15:00.000NaN8.50.9137.0138.0104.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2149-01-01 16:50:07.0001/3/49 18:001/3/49 20:002.0
1357155752148-12-24 16:25:00.000NaN8.00.692.0139.0107.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2149-01-01 16:50:07.0001/3/49 18:001/3/49 20:002.0
2357155752148-12-25 09:12:00.000NaN7.50.7139.0137.0107.0NaNNaNNaNNaNNaNNaN9.8NaN70.091.0245.00.6NaNNaNNaNNaN14.7NaNNaNNaNNaNNaNNaNNaN2149-01-01 16:50:07.0001/3/49 18:001/3/49 20:002.0
3357155752149-01-01 17:37:00.000NaN6.92.7119.0137.0104.0NaNNaNNaNNaNNaNNaN7.70.2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2149-01-01 16:50:07.0001/3/49 18:001/3/49 20:002.0
4357155752148-12-27 08:40:00.000NaN8.40.8180.0138.0104.0NaNNaNNaNNaNNaNNaN10.20.148.079.0208.01.40.21.2NaNNaN18.2NaNNaNNaNNaNNaNNaNNaN2149-01-01 16:50:07.0001/3/49 18:001/3/49 20:002.0
5357155752148-12-26 05:15:00.000NaN7.30.7155.0141.0107.0NaNNaNNaNNaNNaNNaN8.30.153.069.0202.00.6NaNNaNNaNNaN16.0NaNNaNNaNNaNNaNNaNNaN2149-01-01 16:50:07.0001/3/49 18:001/3/49 20:002.0
6357155752148-12-26 19:00:00.000NaN7.60.7112.0140.0106.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2149-01-01 16:50:07.0001/3/49 18:001/3/49 20:002.0
7357155752149-01-01 19:07:00.000NaN6.82.7124.0136.0102.0NaNNaNNaNNaNNaNNaN7.80.120.059.0115.00.8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2149-01-01 16:50:07.0001/3/49 18:001/3/49 20:002.0
8357155752148-12-30 03:01:00.000NaN7.70.986.0139.0109.0NaNNaNNaNNaNNaNNaN9.5NaN29.065.0172.00.8NaNNaNNaNNaN16.8NaNNaNNaNNaNNaNNaNNaN2149-01-01 16:50:07.0001/3/49 18:001/3/49 20:002.0
9357155752148-12-25 15:45:00.000NaN7.90.795.0140.0108.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2149-01-01 16:50:07.0001/3/49 18:001/3/49 20:002.0
stay_idcharttimetotal_proteincalciumcreatinineglucosesodiumchlorideheart_ratesbpdbpmbpresp_ratetemperaturehemoglobinwbcaltastalpbilirubin_totalbilirubin_directbilirubin_indirectphlactatepturineoutputsofa_respirationsofa_coagulationsofa_liversofa_cardiovascularsofa_cnssofa_renalicu_intimevent_startvent_endvent_duration
7014326202322132-05-31 01:30:00.000NaN9.00.8232.0137.0101.0NaNNaNNaNNaNNaNNaN13.413.7NaNNaNNaNNaNNaNNaNNaNNaN12.4NaNNaNNaNNaNNaNNaNNaN2132-05-30 19:27:38.0005/31/32 21:306/3/32 4:0054.500000
7015367622102169-11-06 03:30:00.000NaN8.90.998.0131.0100.0NaNNaNNaNNaNNaNNaN11.07.610.016.066.00.40.20.2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2169-11-05 22:41:00.00011/6/69 15:0011/7/69 3:0012.000000
7016317839972199-07-16 02:44:00.000NaN7.31.9344.0135.0111.0NaNNaNNaNNaNNaNNaN7.519.018.032.056.00.2NaNNaNNaNNaN13.0NaNNaNNaNNaNNaNNaNNaN2199-07-15 22:26:00.0007/16/99 14:007/19/99 3:0061.000000
7017311265442145-06-23 05:05:00.000NaN7.61.669.0137.098.0NaNNaNNaNNaNNaNNaN12.719.347.0200.0102.03.0NaNNaNNaNNaN16.3NaNNaNNaNNaNNaNNaNNaN2145-06-22 21:20:00.0006/23/45 22:006/24/45 9:0011.000000
7018354147222143-11-08 08:27:00.0005.48.40.6108.0138.0101.0NaNNaNNaNNaNNaNNaN12.66.422.019.052.00.70.30.4NaNNaN13.0NaNNaNNaNNaNNaNNaNNaN2143-11-08 06:58:00.00011/10/43 5:3011/11/43 11:0029.500000
7019383070842153-12-18 22:50:00.000NaN10.10.9140.0136.099.0NaNNaNNaNNaNNaNNaN15.77.124.018.057.00.8NaNNaNNaNNaN12.1NaNNaNNaNNaNNaNNaNNaN2153-12-19 02:35:00.00012/20/53 5:0412/21/53 6:0024.933333
7020399290912170-10-18 15:42:00.000NaNNaN0.793.0141.0102.0NaNNaNNaNNaNNaNNaN11.06.68.012.071.00.4NaNNaNNaNNaN11.2NaNNaNNaNNaNNaNNaNNaN2170-10-18 15:21:13.00010/19/70 16:3310/20/70 0:007.450000
7021378638482147-09-20 03:57:00.000NaN8.51.0104.0141.0102.0NaNNaNNaNNaNNaNNaN11.413.8NaNNaNNaNNaNNaNNaNNaNNaN15.3NaNNaNNaNNaNNaNNaNNaN2147-09-19 19:20:00.0009/20/47 19:009/25/47 8:00109.000000
7022330188912180-08-18 02:31:00.000NaN9.01.2111.0135.098.0NaNNaNNaNNaNNaNNaN8.717.858.031.092.00.4NaNNaNNaNNaN15.6NaNNaNNaNNaNNaNNaNNaN2180-08-18 00:52:00.0008/18/80 18:008/19/80 12:0018.000000
7023364294192115-03-31 04:55:00.000NaN8.87.388.0138.0100.0NaNNaNNaNNaNNaNNaN9.020.014.017.077.00.4NaNNaNNaNNaN12.8NaNNaNNaNNaNNaNNaNNaN2115-03-30 21:52:00.0003/31/15 16:004/2/15 14:0046.000000